Nmr quantification of tmao

ABSTRACT

A defined peak region residing between about 3.2 and 3.4 ppm of a proton NMR spectrum of an in vitro biosample is electronically evaluated to determine a level of trimethylamine-N-oxide (“TMAO”). The biosamples may be any suitable biosamples including human serum with a normal biologic range of between about 1-50 μM or urine with a normal biologic range of between about 0-1000 μM.

RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalApplication Ser. No. 61/654,249, filed Jun. 1, 2012, and U.S. patentapplication Ser. No. 13/801,604, filed Mar. 13, 2013, the contents ofwhich are hereby incorporated by reference as if recited in full herein.

FIELD OF THE INVENTION

The present invention relates generally to analysis of in vitrobiosamples. The invention may be particularly suitable for NMR analysisof human urine, blood plasma and serum.

BACKGROUND OF THE INVENTION

Researchers have described the use of trimethylamine containingcompounds, and in particular trimethylamine-N-oxide (“TMAO” or “TMANO”),as risk predictors for cardiovascular disease. See, U.S. PatentApplication Publication 2010/0285517 and Wang et al, Gut florametabolism of phosphatidylcholine promotes cardiovascular disease,Nature, Vol. 472, pp. 57-63 (April, 2011), the contents of which arehereby incorporated by reference as if recited in full herein.

SUMMARY

Embodiments of the invention provide methods, systems, circuits,analyzers and computer program products for NMR quantification of TMAO.

Embodiments of the invention are directed to methods of determining ameasure of TMAO in in vitro biosamples. The methods includeelectronically determining a level of trimethylamine-N-oxide (“TMAO”) ofan in vitro biosample using a defined TMAO peak region having a singleTMAO peak residing between about 3.2 and 3.4 ppm of a proton NMRspectrum.

The method may also include: (a) electronically identifying a definedpH-stable reference peak region in the NMR spectrum of the biosample;(b) electronically identifying a defined calibration peak region in theNMR spectrum of the biosample (the calibration peak region locationchanges based on pH of the biosample); and (c) electronicallycalculating a distance between the reference and calibration peakregions; then (d) electronically determining a location of a TMAO peakfor the defined TMAO peak region based on the calculated distance.

The electronic determination of the TMAO peak region location can becarried out using a defined relationship of a location of the referencepeak region to the calibration peak region with a location of the TMAOpeak.

The method can include, before the determining step, calculating aposition of a TMAO peak region using a fitting region having first sizebetween about 50-100 data points based on a location of a citratereference peak or peaks, then reducing the fitting region to about 30data points centered about the calculated location of the TMAO peak, andelectronically curve fitting the TMAO peak region with a defined curvefitting function or functions.

The defined TMAO peak can be at about 3.3 ppm. The electronicdetermination of the level of TMAO can be carried out by identifying anexpected TMAO peak location using a defined (e.g., linear or other)relationship between a location of a reference peak or peaks and alocation of a pH sensitive calibration peak or peaks, and location of anexpected TMAO peak. A probable actual TMAO peak location can beidentified by: first electronically weighting a region around theexpected TMAO peak location with a defined function; then electronicallyidentifying a highest weighted data point of the weighted region; thenelectronically identifying a probable actual TMAO peak locationcorresponding to location of the highest weighted data point.

The method can include, after the identification of the probable actualTMAO peak location, applying a curve fitting function or functions to acurve fitting region of about 30 to about 50 data points centered aboutthe identified probable actual TMAO peak location to determine the levelof TMAO.

The curve fitting function or functions can be configured to selectivelyallow for one or more neighbors on either side of the probable actualTMAO peak location to account for small misalignments to determine thelevel of TMAO.

The method can also include: (a) electronically identifying a definedcalibration peak multiplet with peaks that vary in distance apart fromone another based on pH of the biosample; (b) determining at least onedistance between one or more of the peaks in the calibration peakmultiplet; (c) electronically determining a pH of the biosample based onthe at least one determined distance; then (d) electronicallydetermining a location of a TMAO peak for the defined TMAO peak regionbased on the determined pH and/or the at least one distance.

The calibration peak multiplet can be a citrate quartet. The electroniccurve fitting of the defined peak region with a defined curve fittingfunction or functions that can be applied to selectively use, zero, one,two or three peak neighbors of the TMAO peak to determine the level ofTMAO.

Determining the measure of TMAO can be carried out to generate ameasurement that is substantially linear in a typical biological rangeof between about 1-50 μM, and may be provided in a lower range ofbetween about 1-10 μM (for blood plasma or serum). Larger ranges may beused for other biosamples, such as between about 0-1000 μM for urine.

The reference peak region can be a glucose peak region.

The reference glucose peak region can be associated with anomericglucose at about 5.20 ppm.

The calibration peak region can be associated with one or more peaks ofa citrate peak multiplet (e.g., quartet).

The biosample can be a human blood plasma or serum sample. The biosamplecan include an acidic pH buffer so that the biosample has a pH betweenabout 5.15 and 5.53.

The calibration peak region can be associated with anomeric glucose atabout 5.20 ppm and the reference peak region can include one peak of thecitrate multiplet (centered) at about 3.7 ppm.

The method may also include: (i) providing containers holding respectivebiosamples with a solution of citrate acid and sodium dibasic phosphatein a defined ratio, with the pH being between about 5.15 and 5.53; (ii)positioning a respective biosample in an NMR probe of an NMRspectrometer; and (iii) obtaining NMR signal to generate the NMR protonspectrum for determining the level of TMAO.

The electronic determination can be carried out by applying curvefitting functions NMR signal associated with the TMAO peak to determinea first level of TMAO, then subtracting a known concentration of theTMAO standard that was added to the biosample to generate apatient-specific level of TMAO.

The ratio of the solution can be between 25:75 to about 50:50(buffer:serum) by volume, but other ratios can be used.

In some embodiments, the method may include (i) providing Containersholding small volumes (e.g., about 50 μL or less) of respectivebiosamples with a solution of citrate acid and sodium dibasic phosphatein a defined ratio with the pH being between about 5.15 and 5.53.

The biosample can be human serum and the obtaining step can be carriedout with and acquisition time (on average) of about 4 seconds per scanwith a plurality of scans per biosample (typically ≧16 scans, and moretypically ≧about 96 scans per biosample, with between about 3-7 minutes,on average, of total acquisition time per biosample).

The method can include generating an output of the level of TMAO with anindication of whether the level is considered normal, high or low and/orwith visual (graphic and/or numerical) indicia of a continuum of risk(e.g., a color graphic of increased risk and/or a TMAO risk score goingfrom low to high), and indicating whether a subject is at risk of acomplication of atherosclerotic cardiovascular disease, and wherein asubject whose TMAO is above a value associated with a defined—percentileof a reference population is at risk of experiencing a complication ofatherosclerotic cardiovascular disease. It is anticipated that theat-risk population would be at or above about the 75^(th) or about the80^(th) percentile.

Other embodiments are directed to computer program products forevaluating in vitro biosamples. The computer program product includes anon-transitory computer readable storage medium having computer readableprogram code embodied in the medium. The computer-readable program codeincludes computer readable program code that evaluates NMR signal in adefined peak region residing between about 3.2 and 3.4 of a proton NMRspectrum of an in vitro biosample to determine a level oftrimethylamine-N-oxide (“TMAO”).

The computer program product can also include: computer readable programcode that identifies a pH-stable reference peak region in the NMRspectrum of the biosample; computer readable program code thatidentifies a defined calibration peak region in the NMR spectrum of thebiosample; computer readable program code that calculates a distancebetween the reference and calibration peak regions; and computerreadable program code that determines a position of a TMAO peak regionto use as the defined peak region to determine the level of TMAO basedon the calculated reference and calibration peak region distance.

The computer program code that determines the position of the TMAO peakregion can include computer program code that uses a definedrelationship of a location of the reference peak to the calibration peakand the calibration peak to the location of the TMAO peak, wherein thecalibration and TMAO peak region locations vary according to pH of thebiosample.

The computer program product can also include computer readable programcode that applies a defined curve fitting function to the defined peakregion using at least one neighbor peak to the defined TMAO peak todetermine the level of TMAO.

The computer program product that evaluates the NMR signal is configuredto generate measurements that are substantially linear in a biologicalrange of between about 1-1000 μM.

The calibration peak region can be associated with glucose at about 5.20ppm, wherein the reference peak region is for a single peak of a citratemultiplet peak region at about 3.7 ppm.

The computer readable program code that evaluates the TMAO peak regionto determine the level of TMAO can include: (a) computer readableprogram code that weights a region around an expected TMAO peak locationwith a defined function; (b) computer readable program code thatidentifies a highest weighted data point of the weighted region as aprobable actual TMAO peak location; and (c) computer readable programcode that applies a curve fitting function or functions to a curvefitting region of about 30 to about 50 data points centered about theidentified probable actual TMAO peak location to determine a level ofTMAO.

The computer program code that applies the curve fitting function orfunctions can selectively allow for one or more neighbors on either sideof the probable actual TMAO peak location to account for smallmisalignments to determine the level of TMAO.

Still other embodiments are directed to an analysis system. The systemincludes an NMR spectrometer (at least one) for acquiring at least oneNMR spectrum of an in vitro biosample; and at least one processor incommunication with the NMR spectrometer, the at least one processorconfigured to determine a level of trimethylamine-N-oxide (“TMAO”) inthe biosample using the at least one proton NMR spectrum based on adefined peak region residing between about 3.2 and 3.4 of the at leastone proton NMR spectrum.

The at least one processor can be configured to (i) identify a pH-stablereference peak region in the at least one NMR spectrum of the biosample;(ii) identify a defined calibration peak region in the at least one NMRspectrum of the biosample; (iii) calculate a distance between thereference and calibration peak regions; then (iv) determine a locationof a TMAO peak region for the defined peak region based on thecalculated distance.

The TMAO peak location can be determined using a defined relationship ofa location of the calibration peak with a location of the TMAO peak,both of which vary according to pH of the biosample, relative to thedistance between the calibration and reference peak regions.

The defined TMAO peak region can be at about 3.30 ppm.

The at least one processor can be configured to apply a curve fittingfunction to the defined peak region using at least one adjacent peakneighbor to the TMAO peak to determine the level of TMAO.

The at least one processor can be configured to generate measurementsthat are substantially linear in a biological range for expected ornormal biological values. The blood plasma or serum range can be betweenabout 1-50 μM, more typically between about 1-10 μM. The urine range canbe between about 0-1000 μM.

The reference peak region can be associated with anomeric glucose atabout 5.20 ppm, and the calibration peak region can be associated withone or more peaks of a citrate peak multiplet.

The system can also include containers holding respective biosampleswith a solution of citrate acid and sodium dibasic phosphate in adefined ratio, the ratio being between 25:75 to about 50:50(buffer:serum) by volume, with the pH being between about 5.15 and 5.53.The respective containers or just the respective sample in a flow cellare held in the NMR probe for under 4 seconds of acquisition time perscan with a plurality of scans to generate the NMR signal for therespective at least one NMR spectrum.

The at least one processor can be configured to identify an expectedTMAO peak location using a defined relationship between a location of areference peak or peaks and a location of a pH sensitive calibrationpeak or peaks, and location of an expected TMAO peak. The at least oneprocessor can be configured to identify a probable actual TMAO peaklocation by (i) weighting a region around the expected TMAO peaklocation with a defined function; then (ii) identify a highest weighteddata point of the weighted region as the probable actual TMAO peaklocation.

The defined relationship can be a defined linear relationship.

The at least one processor can be configured to apply a curve fittingfunction or functions to a curve fitting region of about 30 to about 50data points centered about the identified probable actual TMAO peaklocation to determine the level of TMAO.

The at least one processor can be configured to apply the curve fittingfunction or functions to selectively allow for one or more neighbors oneither side of the probable actual TMAO peak location to account forsmall misalignments to determine the level of TMAO.

Further features, advantages and details of the present invention willbe appreciated by those of ordinary skill in the art from a reading ofthe figures and the detailed description of the preferred embodimentsthat follow, such description being merely illustrative of the presentinvention. Features described with respect with one embodiment can beincorporated with other embodiments although not specifically discussedtherewith. That is, it is noted that aspects of the invention describedwith respect to one embodiment, may be incorporated in a differentembodiment although not specifically described relative thereto. Thatis, all embodiments and/or features of any embodiment can be combined inany way and/or combination. Applicant reserves the right to change anyoriginally filed claim or file any new claim accordingly, including theright to be able to amend any originally filed claim to depend fromand/or incorporate any feature of any other claim although notoriginally claimed in that manner. The foregoing and other aspects ofthe present invention are explained in detail in the specification setforth below.

As will be appreciated by those of skill in the art in light of thepresent disclosure, embodiments of the present invention may includemethods, systems, apparatus and/or computer program products orcombinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is an NMR spectrum of human serum with an expansion of thespectrum showing a location of an NMR peak region for TMAO according toembodiments of the present invention.

FIG. 1B is an NMR spectrum of human urine with an expansion of theregion containing the TMAO peak.

FIG. 2A shows a stacked plot of NMR spectra showing the change in thelocation of the TMAO peak (indicated by the asterisk) changes withchanges in the sample pH according to embodiments of the presentinvention. FIG. 2B is an expansion of a stacked plot of NMR spectrashowing the changes in the TMAO peak location across a narrow pH rangefrom 5.15 to 5.53 according to embodiments of the present invention.Across this range the TMAO peak moves by about 20 Hertz (0.05 ppm atabout 400 MHz).

FIG. 2C is an overlay of the TMAO region of NMR spectra collected, onewith endogenous TMAO only and one with the addition of a defined amountof TMAO added to the buffer solution according to embodiments of thepresent invention.

FIG. 3A is a schematic illustration of an NMR spectrum with a referencepeak region and a calibration peak region used to determine a locationof the TMAO peak according to embodiments of the present invention.

FIG. 3B is a schematic illustration of an NMR spectrum with a referencepeak region and a calibration peak region used to determine a locationof the TMAO peak according to other embodiments of the presentinvention.

FIG. 3C is a schematic illustration of an NMR spectrum with apH-variable calibration peak region used to determine pH and/or alocation of the TMAO peak according to embodiments of the presentinvention.

FIG. 3D is a graph of a weighted function that can be used to identify aprobable actual TMAO peak location according to some embodiments of thepresent invention.

FIG. 4A is a graph of distance between citrate and glucose peak (deltacitrate) across the pH range from 5.15 to 5.45. The data shows thedefined mathematical relationship between the distance between glucoseand TMAO peak (delta TMA) according to embodiments of the presentinvention.

FIG. 4B is a graph of distance between citrate and glucose peak (deltacitrate) in a set of patient samples that have a narrow pH range. Thedata shows the defined mathematical relationship between the distancebetween glucose and TMAO peak (delta TMA) according to embodiments ofthe present invention.

FIG. 5 is an NMR spectrum illustrating alternative locations of glucosepeak regions according to embodiments of the present invention.

FIG. 6A-6F are graphs of curve fitting for different amounts of TMAO ina sample according to embodiments of the present invention. FIGS. 6A and6B illustrate concentrations in an estimated 1^(st) quartile. FIGS. 6Cand 6D correspond to measurements in an estimated 2^(nd) quartile. FIGS.6E and 6F correspond to measurements in an estimated 4th quartile.

FIG. 7A is a graph showing curve fitting of the actual TMAO peak withmultiple TMAO and protein basis functions according to embodiments ofthe present invention.

FIG. 7B is a flow chart of an intelligent TMAO curve fitting functionaccording to embodiments of the present invention.

FIG. 8 is an exemplary pulse sequence using a standard “one-pulse”protocol with WET solvent suppression according to embodiments of thepresent invention.

FIG. 9 illustrates the first pulse sequence in FIG. 8 modified toinclude a CPMG sequence to attenuate signals associated withmacromolecules and large aggregates such as proteins and lipoproteins saccording to particular embodiments of the present invention.

FIGS. 10A and 10B are NMR spectra acquired using the pulse sequence ofFIG. 8 (FIG. 10A) and that of FIG. 9 (FIG. 10B). The broad signals frommacromolecules are reduced in the CPMG pulse sequence according toembodiments of the present invention.

FIG. 11 is a graph of concentration versus CV % for LOQ for 64, 128 and192 scans according to embodiments of the present invention.

FIG. 12A is a graph of % CV versus TMAO concentration (μM) from spikeddialyzed serum according to embodiments of the present invention.

FIG. 12B is a graph of % CV versus TMAO concentration (μM) from patientblood serum samples according to embodiments of the present invention.

FIG. 13A is a graph of the correlation between the NMR determined TMAOconcentrations (μM) in dialyzed serum spiked with known concentrationsof TMAO (μM). The ordinate of the graph indicates the TMAO concentrationbases on gravimetrically determined spiking of the dialyzed plasma. Theabscissa indicates the NMR measurement of the concentration.

FIG. 13B is a graph of correlation between the NMR determined TMAOconcentrations (μM) and those determined by hyphenatedliquid-chromatography mass spectrometry measurements (μM) in patientsamples to assess accuracy of the assay according to embodiments of thepresent invention.

FIG. 14A is a flow chart of exemplary operations that can be used tocarry out embodiments of the invention.

FIG. 14B is a flow chart of an exemplary pre-analytical quality controlevaluation that can be carried out according to embodiments of thepresent invention.

FIG. 14C is a multiplet (reference) peak region of an NMR spectrum thatcan be used for the quality control evaluation shown in FIG. 14Baccording to embodiments of the present invention.

FIG. 14D is an enlarged view of one peak shown in FIG. 14C that can beused to evaluate skew according to embodiments of the present invention.

FIG. 15A is a schematic illustration of an NMR measurement systemaccording to embodiments of the present invention.

FIG. 15B is a schematic illustration of a container with a patientsample and buffer solution for analysis to assess TMAO level accordingto embodiments of the present invention.

FIG. 16 is a schematic illustration of an NMR analyzer according toembodiments of the present invention.

FIG. 17 is a block diagram of a data processing system according toembodiments of the present invention.

FIG. 18 is a schematic illustration of a patient report with examples ofvisual risk indicia associated with different levels of TMAO accordingto embodiments of the present invention.

FIG. 19 is a stacked plot of NMR spectra (chemical shift/ppm) of urineaccording to embodiments of the present invention (the pH goes from 4.62at the bottom to 6.83 at the top).

FIGS. 20A and 20B are graphs of a TMAO peak versus reference peak (FIG.20A shows Creatinine and FIG. 2013 shows Citrate) illustrating arelationship between TMAO and the reference peak according toembodiments of the present invention.

FIG. 21 is a graph of a series of Lorentzian and Gaussian basisfunctions used to model the TMAO signal to determine TMAO concentrationaccording to embodiments of the present invention. FIG. 21 shows a setof the functions with constant height and differential linewidthsQuadratic and linear functions are included to model baseline offsets.

FIG. 22 is a graph of a series of Lorentzian and Gaussian basisfunctions used to model baseline TMAO signal to determine TMAOconcentration according to embodiments of the present invention. Thesebasis functions have constant area but have differential peak heightsand linewidths.

FIGS. 23-26 are graphs of additional examples of basis sets (Lorentzianalone, FIGS. 23, 25 and Gaussian alone, FIGS. 24, 26) according toembodiments of the present invention.

The foregoing and other objects and aspects of the present invention areexplained in detail in the specification set forth below.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Generally stated, embodiments of the invention are directed to NMRassays that can measure the concentration of TMAO in biosamples,typically urine, serum or plasma samples. The concentration can bemeasured by determining the peak area of a defined region in the NMRproton spectra of the NMR signal and translating this into concentrationunits of micromoles (μmol) with a calibration based on TMAO standardsolutions. The concentration of TMAO in the sample can be related to thesubject's risk of developing cardiovascular disease and may also beassociated with other diseases or pathologies.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions, layersand/or sections, these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are only usedto distinguish one element, component, region, layer or section fromanother region, layer or section. Thus, a first element, component,region, layer or section discussed below could be termed a secondelement, component, region, layer or section without departing from theteachings of the present invention. The sequence of operations (orsteps) is not limited to the order presented in the claims or figuresunless specifically indicated otherwise.

The term “programmatically” means carried out using computer programand/or software, processor or ASIC directed operations. The term“electronic” and derivatives thereof refer to automated orsemi-automated operations carried out using devices with electricalcircuits and/or modules rather than via mental steps and typicallyrefers to operations that are carried out programmatically. The terms“automated” and “automatic” means that the operations can be carried outwith minimal or no manual labor or input. The term “semi-automated”refers to allowing operators some input or activation, but thecalculations and signal acquisition as well as the calculation of theconcentrations of the ionized constituent(s) are done electronically,typically programmatically, without requiring manual input.

The term “about” refers to +/−10% of a specified value or number (whichcan the mean or average value). The term “about” with respect to achemical shift ppm value for a particular peak location means +/−0.1 aschemical shifts can change with different sample conditions (e.g. saltand protein concentration, etc.).

The terms “CAD” and “CHD” are used interchangeably to correspond to apatient or subject's risk of developing or having coronary artery and/orcoronary heart disease, respectively. The term “cardiovascular disease”(CVD) refers to a combined outcome that is typically CHD plus stroke.

The term “biosample” refers to in vitro blood, serum, urine, CSF,saliva, bronchoalveolar lavage, fecal or tissue samples of humans oranimals. The biosamples can be from any target subject. Subjects,according to the present invention, can be any animal subject, and arepreferably mammalian subjects (e.g., humans, canines, felines, bovines,caprines, ovines, equines, rodents (mice, rats, hamsters, guinea pigs orothers), porcines, primates, monkeys, and/or lagomorphs). The animalscan be laboratory animals or non-laboratory animals, whether naturallyoccurring, genetically engineered or modified, and/or whether beinglaboratory altered, lifestyle and/or diet altered or drug treated animalvariations. Embodiments of the invention may be particularly suitablefor evaluating human urine and/or human blood plasma or serumbiosamples. The samples may be fasting or non-fasting. In someembodiments, the urine and/or blood plasma or serum sample is a fastingsample, at least about 12 hours of fasting time. In other embodiments,the sample can be obtained after a prescribed diet challenge.

The term “patient” is used broadly and refers to an individual thatprovides a biosample for testing or analysis.

The NMR analysis can be carried out using a small sample size, typicallyabout 500 μL or less, such as between about 100-250 μL. The samples canbe diluted with a defined diluent, such as a pH-changing buffer orbuffers.

The term “exponential function” refers to a mathematical transformationin which the “FID” is multiplied by an exponential function. Typicallydecaying exponentials are used to provide a defined increase in thelinewidth with commensurate increase in signal-to-noise. The term “FID”refers to free induction decay. The time-domain signal is detected anddigitized by the spectrometer after application of the read pulse.Gaussian Multiplication refers to a mathematical transformation in whichthe FID is multiplied by a Gaussian function in order to narrow thelinewidths and increase resolution.

The term “linearity” refers to the ability (within a given range) toprovide results that are directly proportional to the concentration ofthe analyte (here TMAO) in the test sample. The term “limit ofdetection” (“LoD”) refers to the lowest actual concentration at whichthe analyte is reliably detected. The term “limit of quantification”(“LoQ”) refers to the lowest actual concentration at which the analyteis reliably detected (LoD) and at which the uncertainty of the observedresults is less than or equal to the error set for uncertainty. The term“precision” refers to the closeness of agreement between independenttest results obtained under stipulated conditions.

The term “WET” refers to a solvent suppression scheme in which a seriesof radiofrequency and pulsed field gradients are used to reduce thewater signal. See, Ogg, R. J.; Kingsley, R. B.; Taylor, J. S. J. Magn.Reson., Ser. B 1994, 104, 1-10; and Smallcombe, S. H.; Patt, S. L.;Keifer, P. A. S. Magn. Reson., Ser. A 1995, 117, 295-303, the contentsof which are hereby incorporated by reference as if recited in fullherein.

The term “CPMG” refers to a Carr-Purcel-Meiboom-Gill pulse sequence.This is a series of phase defined radiofrequency pulses that providemeans to attenuate signals from large, rapidly relaxing molecules suchas proteins and lipoprotein particles.

The term “AT” refers to acquisition time associated with the length oftime that the FID is digitized in seconds. The term “D1” refers to acomponent of a pulse sequence denoting the delay time prior to the readpulse. The term “Ernst Angle” refers to a read pulse angle for aparticular resonance that yields the maximum signal in a given amount oftime.

The term “clinical” with respect to data measurements means qualitativeand/or quantitative measurements that can be used for therapeutic ordiagnostic purposes, and typically for diagnostic purposes and meets theappropriate regulatory guidelines for accuracy, depending on thejurisdiction or test being performed.

Embodiments of the invention can measure TMAO by NMR over an expectedbiological range of between about 1 to 50 μM, typically 1-30 μM, andmore typically about 1-10 μM, for human plasma and/or serum samples. TheNMR assay may quantify other expected biological ranges for other sampletypes, such as urine, for example, which may have an increased amount ofTMAO over plasma or serum. The urine range may be much larger than therange for human plasma and/or serum such as between 0-1000 μM. The assaycan be linear over the larger urine range of values.

The term “pH buffer” refers to a chemical added to the biosample tocreate a defined pH-induced NMR peak shift in the NMR spectrum. Thebuffer can be any suitable acidic buffer such as acetate and/or citrate.As will be discussed below, one particularly suitable buffer is citratephosphate buffer (e.g., citric acid and sodium dibasic phosphate, e.g.,C₆H₈O₇.H₂O and Na₂HPO₄.7H₂O).

Embodiments of the invention provide an NMR assay with sufficientaccuracy, precision and linearity to provide clinically beneficialmeasures of TMAO.

It is understood that the chemical shift described herein for NMRsignals and peaks are with respect to a spectrometer having an operatingfrequency of about 400 Hz. As is well-known, peak locations measured inppm should remain constant at different field strengths, but thefeatures of the spectrum may differ due to the different resolution andaltered appears of scalar coupling. FIG. 1A shows an example of a fullNMR spectrum of serum taken under standard conditions on the Vantera®clinical NMR Analyzer by LipoScience, Inc., Raleigh, N.C. The expansionshows that TMAO is in a very crowded region which can confound thequantitation. The normal biological range of TMAO in human serum orplasma is between about 1 to about 50 μM which may normally be betweenabout 1-10 μM, but dietary spikes can raise the upper value, typicallyto between about 30-50 μM or even higher. This amount is very low forNMR detection, but this metabolite benefits from the fact that the onlysignal is a singlet that results from 9 magnetically degenerate protons;thus, the ¹H signal concentration is 9 times higher than the absolutechemical concentration.

Urinary TMAO is thought to be highly correlated with serum TMAO levels,after correcting for concentration using the creatinine concentration.FIG. 1B shows the NMR spectrum of urine with an expansion around theTMAO signal. TMAO is present in urine at a much higher concentration. InFIG. 1B, the TMAO level is 540 uM, which is more than about 100 timesgreater than the normal concentrations in serum. Typical urine ranges ofTMAO can be between about 0-1000 μM as noted above.

It is contemplated that the normal biological variability may besufficiently large that only a semi-quantitative test is necessary,e.g., quantitative measures of values associated with a fourth quartileor fifth quintile of a hazard ratio, e.g., the 75^(th) percentile or80^(th) percentile, which may be associated with a concentration ofabout 6.2 μm or greater. In some embodiments, the NMR assay can reliablyquantify to at least the 50^(th) percentile, e.g., about 3.7 μm orgreater for human blood plasma or serum samples. That is, where TMAO isassociated with increased risk or abnormal conditions or disease, theamounts of TMAO in a sample can be greater than normal ranges/values andcan be more precisely measurable than low levels.

Urinary TMAO levels will likely be more influenced by acute dietaryinfluences whereas the serum assay is likely to more reflective of thechronic TMAO levels. An NMR urinary assay has higher concentrations ofTMAO, than is present in serum or plasma, which may allow a similar-highvolume throughput. While discussion of diluents, buffers and samplepreparation discussed below are applicable to multiple biosample types,particular evaluation protocols for urine biosamples for TMAO will bediscussed further below, see, e.g., FIGS. 19, 20A and 20B.

Typically, the NMR analyzer 22 (FIGS. 13A, 14) can diagnosticallyanalyze at least about 400, and more typically at least about 600,samples per twenty-four hours. See, e.g., U.S. Pat. No. 8,013,602 for adescription of a suitable NMR analyzer 22 (FIGS. 13A and 14), thecontents of which are hereby incorporated by reference as if recited infull herein. FIG. 2A shows an expansion of the serum spectrum shown inFIG. 1A with the pH adjusted. Thus, referring to FIG. 2A, in order toresolve the TMAO peak, marked with an asterisk, from the overlappingmetabolites, the pH of the biosample is altered over pH ranges from 2.82at the top to 7.9 at the bottom. It is clear that there is a significantshift of the peak between pH 4.6 and 5.6. FIG. 2B shows the chemicalshift behavior of this TMAO peak over the narrower pH range from 5.15 to5.53. The region in the NMR spectrum between 3.2 and 3.4 ppm isrelatively open, i.e., with fewer significant interferences to TMAOconcentration. Thus, in some embodiments, a target pH for the sample canbe set to position the NMR TMAO peak between about 3.2 and 3.4, whichcan be carried out, for example, by adjusting the pH to be between about5.15 and 5.53. In some particular embodiments, the pH of the sampleundergoing analysis can be set to about 5.3 to put the TMAO peak inabout the center of the open region.

When analyzing biosamples, such as urine or serum, for example, otherpeaks may overlap with the TMAO peak but they can be modeled by a peakfinding and quantitation algorithm so that accurate measurements can beobtained.

FIG. 2C is an overlay of the TMAO region of two NMR spectra collected,one with endogenous TMAO and one with the addition of a defined (10 uM)amount of TMAO added to the buffer solution. This region displays anumber of other background signals which could in some instancesconfound the quantitation of the TMAO. The addition of a known quantityof TMAO in the buffer solution insures that the TMAO peaks will be thelargest peak in this region and therefore can be unequivocallyidentified. The standard addition also aides in the fitting of the TMAOpeak such that the effects of overlapping resonances are minimized inthe peak fitting routine. As a known amount of the standard TMAO (e.g.,“standard” in a known concentration is used), once the TMAOconcentration is measured from the enhanced peak, the concentrationadded by the TMAO standard diluent is subtracted to yield the TMAOconcentration resident in the sample. It is contemplated that a TMAOstandard concentration between about 1-100 μM can be used for TMAObiosample evaluations, typically between about 5-20 μM, and moretypically about 10 μM.

As will be discussed below, the diluents and/or buffer (including theTMAO standard) can be provided with a defined final concentration toyield a defined blood plasma or serum to buffer ratio, typically of50:50 or greater, and more typically 75% serum and 25% buffer but morebuffer than serum can also be used. However, other final concentrationvalues and ratios may be used as discussed below.

Referring now to FIG. 3A, in some embodiments, to facilitate locatingthe TMAO peak region 10, recognizing that commercial application may besuch that some samples may have slight variations in pH, a pH stablereference peak region 20 in the NMR spectrum of the biosample can beutilized. In some embodiments, the reference peak region is associatedwith glucose which is in the biosample. This does not require that areference analyte be added to the sample to create the reference peak20. However, it is contemplated that other reference peak regions may beused using added reference material or other pH stable constituents inthe biosample. For some biosamples, such as urine, other added ornatural (internal shift standards) or reference compounds can be used,e.g. TSP (the sodium salt of trimethylsilylpropionic acid (includingdeuterated version), DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid),and the like can be used as well as other pH stable chemicals and/orcompounds.

As shown in FIG. 3A, a defined calibration reference peak region 30 withsignal intensity greater than TMAO can also be used. The location of thecalibration peak region varies or changes with pH. The distance “d1”between the stable reference peak region 20 and the calibration peakregion 30 (one or more of the citrate quartet peaks) can be calculated.Then, based on a defined relationship between the location of thecalibration peak region 30 and the relative position of the TMAO peakregion 10, the distance “d2” can be determined which identifies thelocation of the TMAO peak region 10. The calibration peak region can beone or more peaks of a citrate quartet peak region. However, other pHvarying calibration peak regions may also be used.

FIG. 3B illustrates a method similar to that shown in FIG. 3A, but theTMAO peak location can be calculated based on a distance d2 betweenglucose and TMAO rather than citrate and TMAO.

In some embodiments, as shown in FIG. 3C, the location of the TMAO peakregion can be determined without the use of the pH-stable reference peakregion. As shown, two or more peaks of a pH-variable calibration signal,e.g., the individual peaks of the citrate multiplet 30 can also give pHinformation, as the distance between these peaks varies as a function ofpH. The distance between peaks can be used to determine the TMAO peaklocation. The distance can be calculated based on leading edges,trailing edges or a center of the peak. This embodiment does not requirea separate reference compound/reference peak region 20 as thecalibration peak region can be used to define pH that is used to definethe TMAO peak location. As shown, there are four peaks associated withthe calibration multiplet, shown as P1-P4 (right to left), and adistance “Δ” between each adjacent peak and a distance Δ between othercombinations of the peaks, e.g., between any or combinations of P1-P2,P2-P3, P3-P4, P1-P3, P1-P4, P2-P4 and the like including a summativedistance between each or combinations of peaks that may be used toidentify a pH level. This distance changes as the chemical structurebends in response to pH level of the biosample. Thus, one or moredistances between one or more of the peaks in the citrate multiplet canbe used to identify pH level, which can then be used to locate the TMAOpeak. The location of the TMAO peak 10 can be based on a look-up tableor other computational model that correlates spacing distance to pHlevel and pH level to TMAO peak location, or may be identified bycalculating a distance Δ2 between one or more of the citrate peaks andthe projected TMAO peak location.

In some embodiments, recognizing that across the pH range from 5.15 to5.45, the downfield peak of the citrate shifts downfield by about 52points (14 Hz), the spectra can be acquired with a sufficient digitalresolution, such as, for example, about (16384 pt)/(4496.4 Hz)=(3.64pt/Hz).

In particular embodiments, the reference peak region can be an anomericglucose peak region 20 at about 5.20 ppm with glucose peaks that arehighly stable to pH and display substantially no shift across thisrange. The distance between the citrate peak region 30 is linearlycorrelated to the distance between one or more of the citrate peaks andthe TMAO peak. This relationship is shown in FIGS. 4A and 4B. The TMAOpeak 10 can be accurately located based on the location of the citratepeak(s) or the glucose peak(s). The locations of the three peak regions10, 20, 30 and the defined mathematical relationship is believed to beindependent of temperature of the sample during analysis andspectrometer field strength.

Y=1.5449x+2819.9  EQUATION (1A)

where Y is the distance of the TMAO peak from glucose, and x is thedistance between the calibration (citrate) peak and the reference(glucose) peak.

Y=1.4924x+2626  EQUATION (1B)

In summary, a set of samples around the expected range can be prepared.The spectra can be analyzed and the distance between the invariantglucose and the pH sensitive citrate can be measured. The distance fromthe invariant glucose and TMAO can be measured and a definedmathematical relationship between the two can be determined. Equations1A and 1B are examples of equations for determining TMAO peak locationusing glucose. However, it is noted that experimental conditions (pHbuffers, NMR spectrometers and the like) can vary and the TMAO peakdistance can be calculated from one or more citrate peaks rather thanglucose. Thus, these Equations are by way of example only and anysimilar equation that results in an R² of ≧0.9 will be consideredequivalent to these defined mathematical relationships.

While the glucose peak region 20 (multiplets that are centered) at 5.20ppm was used in this example, other reference peak or peak regions maybe used. In some embodiments, one or more other glucose peak regions maybe used such as one or more peaks of glucose multiplets “G” centered atone or more of about 4.6, 3.9, 3.8, 3.7, 3.5, 3.4 and 3.2 ppm as shownby the lower darker lines in FIG. 5.

As noted above, in some embodiments, the location of the TMAO peakregion 10 can be determined using one or more of the citrate peaks asthe calibration reference peak/peak region. In some embodiments, onepeak of the citrate multiplet is found at 3.7 ppm. Given that thetypical biosample with the added pH buffer contains a largeconcentration of citrate (e.g., typically the buffer is at least about25% by volume), these citrate peaks are easy to find electronically asthey are among the largest peaks in the spectrum. The distance betweenthe anomeric glucose peaks 20 and the citrate peaks 30 is related to thedistance between the citrate 30 and TMAO peaks 10. This definedmathematical relationship has been shown to robustly determine thelocation of the TMAO peak within approximately 10 data points.

Given the low signal to noise of the TMAO at the low concentration,finding the actual TMAO peak can be challenging. In some embodiments,the TMAO peak 10 can be determined to be the 1^(st) peak maximum that isfound near the starting location determined by the calibration peakevaluation. However, it is contemplated that other protocols oralgorithms can be used to effectively and efficiently determine thelocation of the TMAO peak, especially with low TMAO concentrations wherethe noise and low concentration interferences are more confounding. Forexample, when the signal to noise is quite low, the peaks 10 may not bereadily distinguishable from the noise. In some embodiments, asdescribed above, TMAO can be added to a buffer to “amplify” the signaland/or insure that TMAO will be the largest peak in the region ofinterest.

In some embodiments, the electronic determination of the expected TMAOpeak location can be carried out using a defined linear relationshipbetween the location of the reference peak, the location of the pHsensitive calibration peak, and the location of the TMAO peak. The(probable) actual TMAO peak location can be identified by weighting theregion around the expected TMAO peak location with a Gaussian or similarfunction such as, but not limited to, triangular and parabolicfunctions.

The probable actual TMAO peak location can then identified as thehighest weighted data point. The algorithm can mathematically emphasizethe search for the actual peak around that location. FIG. 3D shows anexample function in which the highest point on the curve corresponds tothe probable actual TMAO peak location. The search for the actual TMAOpeak will therefore be weighted toward that location. Thus, the highestweighted data point of the weighted region is defined as the probableactual TMAO peak location which can be used as the center point of thefitting region to determine the level of TMAO.

The fitting region can include between about 30 to about 50 data pointscentered about the calculated (probable) actual location of the TMAOpeak. The fitting region can be electronically curve fit with a definedcurve fitting function or functions. The curve fit can selectively allowfor one or more neighbors on either side to account for smallmisalignments to determine the level of TMAO.

FIGS. 6A-6F illustrate curve fitting of TMAO peaks from patientbiosamples with concentrations in the 1^(st) (6A, 6B), 2^(nd) (6C, 6D)and 4th (6E, 6F) quartiles. The quartile ranges are by way of exampleonly and are estimates from literature values. See, e.g., Wang et al.,Gut flora metabolism of phosphatidylcholine promotes cardiovasculardisease, 58 Nature, Vol. 472, Apr. 7, 2011, the contents of which arehereby incorporated by reference as if recited in full herein. Thesevalues may change with additional evaluation of clinical data or overtime. The TMAO peaks can have a narrow width of less than 2 Hz, moretypically about 1.5 Hz or less, e.g., about 1.2 Hz to about 0.6 Hz, andthe peak shape can vary and may have an asymmetric shape, e.g., anon-Lorentzian shape. In order to account for small differences in thelinewidth of the TMAO peak, a curve fitting technique can be used. Theline shape and linewidth can vary which can make quantitation difficult.The fits in FIGS. 6A and 6B are not ideal and quantitative measures ofTMAO at this low end may be unreliable. However, the fits in FIGS. 6Aand 6B yield low values that would accurately categorize the patient asbeing in the 1st quartile or quintile, for example.

It is contemplated that patients having high TMAO values (in the 4^(th)quartile or 5^(th) quintile, for example) relative to a definedpopulation are considered to be “at-risk” or as having an elevated riskrelative to the population norm.

In order to account for small differences in the linewidth of the TMAOpeak, a curve fitting technique can be used. The curve fitting may usedifferent sets of basis functions that can vary biosample to biosample,which can include none, or one or more neighboring TMAO peaks thatreside on adjacent the main TMAO peak.

Once the TMAO peak 10 has been found, it can be computationally fitusing one or more defined fitting functions as shown in FIG. 7A. The twocomposite lines shown at the upper portion represent an actual line,marked “actual” and a fitted line, marked “fitted” and shown in brokenline. As shown on the bottom of FIG. 7A, the TMAO fitting functions caninclude a primary function F1 and at least one secondary function F2which may be programmatically used selectively if the curve fitting isnot sufficiently accurate for a particular biosample. The basisfunctions can be pre-defined and selected for use programmatically basedon certain defined measurement or curve fitting decisions and can beconfigured to include none (only the primary basis function F1), or oneor more of different pre-defined secondary curve fitting or secondarybasis functions which may use one, two or three neighboring peaks of theTMAO peak.

The analysis circuit or module (e.g., at least one digital signalprocessor) can be programmed or otherwise configured to decide whetherone or more secondary curve fitting functions is appropriate for anyparticular biosample. Thus, the analysis may vary biosample to biosamplebased on a defined set of alternate curve fitting functions. The one ormore secondary curve fitting functions F2 may use one or moreneighboring TMAO peaks to help more accurately or reliably fit thisregion.

The fitting can include a set of basis functions that include a TMAOpeak (e.g., the primary function F1 and optionally one or more secondaryfunctions F2) as well as a quadratic function that accounts for theresidual protein baseline interferences (bottom of FIG. 7A) that survivethe pulse sequence. The TMAO basis function can be an experimentallyacquired spectrum of TMAO processed with consistent parameters to theactual spectrum. Computationally derived TMAO basis functions, i.e.,specified functions comprised of Lorentzians, Gaussians or somecombination of both can also be used.

The lineshape deconvolution can be achieved with a non-negative leastsquares fitting program (Lawson, C L, Hanson R J, Solving Least SquaresProblems, Englewood Cliffs, N.J., Prentice-Hall, 1974). This is avoidsthe use of negative concentrations which will lead to error dueespecially in low signal to noise spectra. Mathematically, the lineshapeanalysis was described in detail for lipoproteins in the paper by Otvos,J D, Jeyarajah, E J and Bennett, D W, Clin Chem, 37, 377, 1991.Referring particularly to the equation in the left column of page 379.In this equation, Vji can represent the TMAO peaks (including main peakand optionally one or more neighbors) and Vki can be the proteincomponents. A synthetic baseline correction function may also be used toaccount for baseline offsets from residual protein components. This cantake the form of a quadratic or other polynomial function. Weightingfactors are determined and the fit can be optimized by minimizing theroot mean squared deviation between the experimental and calculatedspectrum. See also, U.S. Pat. No. 7,243,030, the contents of this patentand the Otvos et al. article are hereby incorporated by reference as ifrecited in full herein.

The relative TMAO concentrations determined have no physical meaning. Alinear calibration function can be determined which relates the integralunits from the spectrometer to micromolar concentration values. Thecalibration function is determined by measuring the signals of sampleswith known concentrations of TMAO. These are typically samples preparedby spiking TMAO into extensively dialyzed plasma which has all of thesmall molecule metabolites dialyzed away.

FIG. 7B is a flow chart that illustrates exemplary operations or stepsthat can be used for selecting basis functions for the TMAO curvefitting analysis. The boxes shown with a broken line perimeter indicatesthat these operations or steps can be generated prior to analysis ofrespective biosamples. As shown, the basis functions for the proteinbackground and target analytes are defined (block 60, 62) and the designmatrix from each basis function is also defined (block 65). A biosamplespectrum is obtained (electronically read or otherwise provided orobtained) (block 70). The location of the analyte (e.g., TMAO) peak isdetermined using either blocks 71, 73 (the reference peak andcalibration peaks are determined, then a pH dependent relationship isused to determine the location of the analyte peak), or a calibrationmultiplet is used with distance between peaks varying according to pH(block 75). The basis functions and baseline functions are used innon-negative least squares fitting of the analyte peak region (block 80)which can selectively include the use of one or more of several definedbasis functions for a single analyte to compensate for non-ideallineshapes that maybe present in a particular biosample (block 82). Apolynomial function can be applied for a baseline correction (block 84).An analyte conversion factor can be applied to determine a molarconcentration of the biosample (block 86). This conversion factor can bebased on a calibration correlation defied using known analyte samples ofdifferent concentrations (block 87). The final concentration of theanalyte can be output, such as to a patient report (block 88).

It is technically challenging to fit peaks in experimental spectra fromcomplex mixtures such as biofluids, where signals from other componentsof the sample can interfere with the signal of interest and peaks canhave non-ideal lineshapes. Factors including, but not limited to,differential protein binding, ionic composition and field inhomogenitycan lead to non-Lorentzian, sometimes asymmetric peak shapes. To fitthese types of peaks, embodiments of the invention provide the option touse additional analyte basis functions that are placed on either side ofthe main peak. Where used, the neighbors can be placed in one pointincrements on either side of the main peak, with up to 3 neighbors oneach side. The number of neighbors allowed in a fitting protocol can beset prior to the analysis and is dependent upon the spectralcharacteristics of the assay including signal to noise ratio andpotentially confounding signals. The contribution of the main peak plusthe neighbors as well as the protein basis functions and baselinecorrection function can be evaluated using the non-negative linear leastsquares algorithm (block 80). Thus, deconvolution of the small, single,pH-dependent TMAO peak typically takes place after determining its exact(or substantially exact) location based on a defined mathematicalrelationship between the pH-dependent reference and TMAO.

In order to deconvolute the TMAO peak located upfield from the waterpeak, a sixty data point search window can be established frompredetermined parameters in the program setup menu. The search windowcovers all the possible TMAO locations across all (normal) patientsamples. The analysis can use a pH-independent reference present inpatient samples, such as the anomeric glucose peaks, to determine thelocation of this 60 data point window in the spectrum. The approximatelocation of the glucose doublet (located downfield of the water peak) isspecified within the program and a least squares fit is performed tofind an exact match between the doublet and a Lorentzian lineshape.However, as noted above, a calibration reference multiplet canalternatively or also be used.

Because the TMAO peak (located upfield from the water peak) often has avery small amplitude, it is difficult to locate its position accurately,especially in the presence of other analytes with similarconcentrations. However, it is possible to determine the exact locationof the TMAO peak relative to a citrate reference peak located upfieldfrom the TMAO resonance, since there is a defined mathematicalrelationship relating the location of the pH dependent downfield citrateresonance and the separation of the citrate and TMAO peaks. The analysislocates the position of one or more of the citrate peaks, again using aleast squares fit with a Lorentzian lineshape. This location can beentered into an empirically determined function that calculates theposition of the TMAO peak.

After locating the position of the TMAO peak, the size of the fittingregion can be reduced, typically to about 30-50 data points, centeredaround the calculated location of the TMAO peak. The size reduction fromthe larger search window to the smaller fitting window can diminishpotential interferences from other metabolites. Finally, the analysismodel can employ a single real TMAO basis component to deconvolute thepeak while also allowing for one neighbor on either side to account forsmall misalignments. In addition, the minimal protein baseline presentin the TMAO fitting region can be modeled with three quadraticequations: positive, negative and zero (a line). The least squares fitcan be performed with a 30 data point analyte vector (the spectrum inthe 30 point window) and a 30×6 design matrix, consisting of three TMAObasis vectors (the TMAO basis component and its neighbors—shifted by asingle data point to each side) and the three baseline correctionvectors. The fitting coefficients are generated from a Lawson-Hansennon-negative least squares QR fit (on just the thirty data point fittingregion), resulting in coefficients that are then multiplied byconcentration factors and combined to generate the final TMAOconcentration.

FIGS. 3 and 4 illustrate one embodiment that can find the location ofthe TMAO peak by calculating the distance between glucose and citrateversus the distance between glucose and TMAO. In other embodiments, theTMAO location can be based on the distance between citrate and TMAO. Inthe embodiment discussed with respect to FIGS. 3 and 4, for example, thefollowing protocol can be used.

1. Find the pH stable glucose2. Determine distance to citrate3. Plug distance into defined linear equation (e.g., Equation 1A, 1B) as“x”4. Calculate distance from glucose to TMAO

As noted above, a predefined established relationship for the Δ betweenglucose and citrate vs. the Δ between glucose and TMAO can be used tocalculate TMAO location. For example, as shown in FIGS. 3, 4, thecitrate position relative to glucose can be determined by subtractingthe position of citrate from that of glucose per Equation 2.

a. 7272−10908=−3636  Equation 2

b. The ‘citrate position’ can be inserted into the defined linearequation (along the lines of Equation 1A or 1B), e.g.:y=1.4924(−3636)+2626. This value, y=−2800 defines the TMAO distance fromglucose.

c. The actual TMAO position can then be calculated.

7272−x=−2800  Equation 3

Thus, in this example, x=10072, which is the TMAO position (peakcenter).

The temperature for this assay can be any appropriate temperature,typically between about 20 degrees C. to about 47 degrees C. However,measurement of TMAO does not require an elevated temperature. Somepreliminary examinations have indicated a very slight increase insensitivity when the NMR assay is run at 25 degrees C. This smallimprovement is not likely to have a significant impact in overall assayperformance. If this assay were to be run at a different temperature,then all TMAO assays can be run at one time i.e., in batches, to avoidany potential need for frequent and time consuming temperature changingand equilibration when performing tests at other temperatures.

The pulse sequence parameters can include any appropriate parametersincluding solvent suppression scheme, pulse angle and acquisition time.However, generally stated, in some particular embodiments, the NMRsignal acquisition time per scan, for any one biosample, can be betweenabout 2-4 seconds (on average) and typically between about 3-4 seconds(on average), such as about 3.07 seconds (on average). The NMR analyzermay be configured to obtain at least 16 scans per biosample, typicallybetween 16-256 scans, such as ≧64 scans, and more typically ≧96, such as96 scans or 128 scans with at least about 16K data points collected overa 4400 Hz sweep width, per sample, to obtain the NMR data used tomeasure TMAO.

One element in the pulse sequence is the solvent suppression scheme. AWET solvent suppression scheme uses a series of shaped pulses and pulsedfield gradients over the course of 80 ms. The 1D NOESY-presat schemeuses the first increment of a 2D Nuclear Overhauser Effect Spectroscopy(NOESY) experiment (Beckonert, O.; Keun, H. C.; Ebbels, T. M. et. al.Nat. Protoc. 2007, 2, 2692-2703). In this scheme, a continuous lowpower, frequency selective pulse on water resonance is applied during D1and ‘mixing’ time. The PURGE solvent suppression scheme (Simpson, A. J.;Brown, S. A. J. Magn. Reson. 2005, 175, 340-346) uses a continuous lowpower, frequency selective pulse on water resonance, relaxationgradients and echoes to attenuate the water signal.

The performance of all three sequences (and potentially other sequencesknown to those of skill in the art) is sufficient to achieve consistentspectra. One advantage of the WET sequence is that it does not involveany low power saturation period which could perturb the protein baselinevia spin diffusion. It also does not have any significant delays whichcould lead to signal attenuation via relaxation.

As is well known, a standard presaturation (“Presat”) pulse sequence canbe used to obtain the NMR spectrum for analyzing the TMAO signal. Thispulse sequence involves a selective low power pulse targeting the waterresonance and lasting several seconds. This is well established in NMRpractice and is a robust and reliable method to attenuate the watersignal.

In some embodiments, the WET water suppression scheme can be used. TheWET sequence involves a series of short selective pulses targeting thewater resonance. The entire scheme is prefixed to the pulse sequence asis the Presat, but only requires 80 ms. The other advantage of the WETsequence is the fact that this sequence imposes only a minimalperturbation on the protein signals. Due to the length of a typicalPresat sequence, some of the solvent saturation can be transferred tothe protein which can lead to inconsistent contributions of the proteinto the baseline. Other solvent presaturation schemes can be used, e.g.,a PURGE sequence.

FIG. 8 illustrates a standard “one-pulse” sequence with WET solventsuppression. This leads to a spectrum in which the signals from the highconcentrations of proteins and macromolecular aggregates, e.g.lipoprotein particles, dominate the spectra (FIG. 10, upper profileline). The lower concentration, small molecule metabolites are greatlyobscured in these spectra. As shown in FIG. 8, the WET solventsuppression scheme (Smallcombe, S. H.; Patt, S. L.; Keifer, P. A. J.Magn. Reson., Ser. A 1995, 117, 295-303) includes a series ofsolvent-directed selective pulses and pulsed field gradients followed bya read pulse and an acquisition time during which the signal isdigitized for a fixed amount of time.

The second sequence shown in FIG. 9 incorporates a CPMG pulse train orsequence. This pulse train includes a series of refocusing π pulses (180degrees) during which the signals from large, rapidly relaxing moleculesare attenuated. The duration of this pulse train can be optimized tominimize the background signals from macromolecules, e.g. proteins andlipoprotein particles, while maintaining most of the signal intensityfrom the small molecules such as TMANO. This value is set to 100 ms.Thus, the CPMG pulse train shown in FIG. 9 (for CPMG spectrum in FIG.10B, upper profile line) is designed to attenuate the signals from themacromolecules which facilitate the detection of many more smallmolecule metabolites. This pulse sequence relies on the fact that thesignals from macromolecules relax much faster than small molecules. Thespin-echo train occurring can occur after the read pulse and is designedto maintain the small molecule magnetization while the signals from themacromolecules relax back to equilibrium. The duration of this spin echotrain relates to the degree of attenuation of the macromolecule signals.This delay is typically set to around 100 ms. It is expected that therewill be little perceptible change in performance over the range fromapproximately 60 ms to 150 ms. However, below 60 ms the proteinattenuation may be compromised and above 150 ms the overall signalintensity loss from relaxation may be unsatisfactory.

Comparisons of signal to noise and assay performance were marginallybetter with the CPMG over the WET. However, it is contemplated that theuse of a CPMG sequence can allow the detection of many more metabolitesshould the assay composition expand in the future. CPMG sequences arewell known to those of skill in the art.

In order to obtain an increased (e.g., maximum) signal from a molecule,a 90 degree pulse can be used and the time between these pulses shouldbe in excess of 10 times the longitudinal relaxation time (T1) of thesignal. The T1 for TMAO in dialyzed plasma was determined to be about2.4 seconds. This is not a time efficient means to maximize the signalintensity so a compromise between pulse length and inter-pulse delay canbe made. The relationship between signal intensity, T1, and pulse lengthis given by the Ernst angle equation. The first step in using thisequation is to define the length of the entire pulse sequence. Thelength is defined by the required solvent suppression period, the CPMGdelay and the length of the data acquisition period needed to providethe required digitization of the FID. The Ernst angle equation is asfollows:

Cos(theta)=exp−(total delay)/T1  EQUATION (4)

In equation (4), the total delay equals the d1 delay (including solventsuppression), the CPMG time, plus the acquisition time. T1 representsthe longitudinal relaxation time of the analyte signal of interest. Itis noted that in front of the parentheses containing “total delay” is anegative sign. Solving for theta will give an optimal flip angle.

Current results indicate that the optimal pulse flip angle for TMAO isabout 70 degrees. The equation is relatively insensitive in this regionso it is unlikely that small errors in calibration or small differencesin the T1 due to specific sample composition will lead to significantinefficiency.

As noted above, the acquisition time (AT) is the time that the FID isdigitized. The duration of AT is determined by both the relaxation timeof the signal(s) being quantified and the required digitization. If therelaxation time of the signal being examined is longer than the AT thenthe FID will become truncated resulting in a signal with poor shape. Asmentioned above, the T1 relaxation time for TMAO is about 2.4 seconds inserum and thus the acquisition time should be at least that long. Thedigitization rate of the spectrometer, i.e. the number of points takenper second of acquisition time, is determined by the sweep width of thespectrum. In some embodiments, a desired digital resolution uses atleast 16K data points that are collected over the 4400 Hz sweep width.This can employ between about 2-4 seconds, typically about 3.07 seconds,of AT per scan and a plurality of scans can be used per biosample suchas between 16-384 scans, typically ≧16 scans, more typically ≧64 scans,such as ≧about 96 scans, such as 96 scans, 128 scans, and 192 scans.

The most direct way to increase the detection sensitivity is to increasethe number of scans. In some embodiments, the TMAO assay can be carriedout on samples at about 47 degrees C. so that this assay can be easilyinterleaved with the current LipoProfile® assay. However, as the numberof scans increases, the residence time of the sample in the probe at 47degrees increases and the samples may become denatured. The challenge isto achieve the requisite signal-to-noise ratio for the TMAO peak toallow accurate and precise quantification over a desired biologicalrange (at least those with adverse clinical association).

The data can optionally be collected in blocks of 8 so the assayperformance can be evaluated considering scans in multiples of 8. FIG.11 shows the assay performance using spiked dialyzed serum with 64, 128and 192 scans. Dialyzed serum is an “ideal” matrix in that there are noconfounding small molecule metabolites.

The current data shown in FIGS. 12A and 12B indicate that with 96 scansthe LoQ is near 2.0 μM. The LoQ for actual patient samples is near 3.5μM. The medical decision limit, indicated in the Nature paper by Hazen(incorporated by reference above), is approximately 6.1 μM. Theperformance with 96 scans is sufficient to achieve a quantitative assaythat can quantify TMAO in the clinically relevant range. Lower numbersof scans may also be suitable such as ≧16 scans, particularly dependingon the relevant clinical decision levels and/or curve fittingperformance. Currently, the LoQ of the 128 scan assay is close to the25^(th) percentile (2.43 μM) and the LoQ for the 96 scan assay is closeto the 50^(th) percentile (3.67 μM). Given the biological variabilityand the fact that a current medical decision point is at the 75thpercentile (6.18 μM), a 96 scan assay may be sufficient. This data isplotted as the % CV versus concentration. The data are fit with a simplepower function as indicated on the plot and, from this, concentrationyielding a 20% CV is calculated. This value is the accepted value forthe LoQ. Concentrations below the LoQ cannot currently be quantitativelyreported with a high degree of confidence (but may be qualitativelyreported). The values shown in FIGS. 12A and 12B may change as the assayis optimized.

The biosample can comprise any suitable pH-buffer alone or with otherbuffers as noted above. The buffer(s) can be present with a buffer toserum or plasma ratio of any one of the following (or any number therebetween) 10:90, 15:85; 20:80, 25:75, 30:70; 35:65, 40:60, 45:55, 50:50and even 60:40 (or other values where there is more buffer or buffersthan sample). However, embodiments of the invention use more sample byvolume than buffers or other additives, e.g., reference or calibrationadditives. In certain embodiments, a buffer can maximize the amountserum or plasma in the biosample (e.g., provide a buffer to serum orplasma ratio of 45:55 or greater). In some particular embodiments, thesample comprises a 25:75 (buffer:serum) sample composition that is easyto prepare and provides a significant increase in sensitivity over a50:50 composition. It is contemplated that, in some embodiments, lowvolume biosamples may be analyzed, such as ≦50 μL. If so, it may besuitable to formulate the biosample to have a greater amount of bufferrelative to serum or other biospecimen (e.g., saliva, CSF) such as, forexample, 75:25 (buffer:biospecimen).

A buffer can include one or more of albumin, glucose, citrate, acetateor other acidic compounds as well any of the well established chemicalshift or quantitation references such as formate,trimethylsilylpropionate (and isotopically labeled isomers), and EDTAfor example.

Exemplary pH buffers include, but are not limited to, acidic buffers,such as, but not limited to, a citrate, a phosphate, and/or an acetatebuffer. For example, the biosample can comprise a citrate buffer and/orcitrate phosphate buffer having a pH from about 2.6 to about 5.5 andcomprising citric acid, sodium citrate, and/or sodium phosphate. Otherexemplary buffers include, but are not limited to, an acetate bufferand/or an acetate phosphate buffer having a pH from about 3.7 to about5.5 and comprising acetic acid, sodium acetate, and/or sodium phosphate.In some embodiments, the biosample comprises a citrate phosphate buffercomprising citric acid and sodium dibasic phosphate. Typically, thebuffers are mixed with deionized water and are added to the biosample todilute the sample by a defined amount. However, the chemical buffer(s)may also be added directly into a liquid or tissue biosample.

In some embodiments, the biosample comprises a citrate phosphate buffercomprising citric acid monohydrate and sodium dibasic phosphateheptahydrate. Citric acid monohydrate can be present in a citratephosphate buffer in an amount from about 160 mM to about 170 mM, or anyrange therein, such as, but not limited to, about 164 mM to about 167mM, about 164.4 mM to about 165.6 mM, or about 165.6 mM to about 166.7mM. Sodium dibasic phosphate heptahydrate can be present in a citratephosphate buffer in an amount from about 260 mM to about 275 mM, or anyrange therein, such as, but not limited to, about 267 mM to about 272mM, about 267.2 mM to about 269.6 mM, or about 269.6 mM to about 271.9mM. In some embodiments, citrate phosphate buffer can comprise about 160mM to about 170 mM citric acid and about 260 mM to about 275 mM sodiumdibasic phosphate heptahydrate. In certain embodiments, the biosamplecomprises a citrate phosphate buffer comprising about 165.6 mM citricacid monohydrate, about 269.6 mM sodium dibasic phosphate heptahydrate,and deionized water. In some embodiments, a citrate phosphate buffer hasa pH of about 4.65.

The sample may be prepared for analysis shortly before analysis by theNMR spectrometer (manually or automatically with a sample handler) or ata remote, pre-processing or collection site. For example, a TMAOanalysis container can be pre-loaded with the amount and or processedfor analysis. In other embodiments, the collection container itself canbe pre-loaded with the buffer in a range and the container marked forsample collection level to form the desired buffer:serum volume so thatthe appropriate amount of biosample is collected in the container.

FIGS. 13A and 13B are graphs assessing the accuracy of the assay bycomparing values from gravimetric measure of TMAO into spiked dialyzedserum versus NMR and NMR versus MS determined TMAO in patient samples.The agreement of NMR in both graphs is acceptable with R2 values greaterthan 0.9.

FIG. 14A is a flow chart of exemplary operations that can be used tocarry out embodiments of the present invention. NMR spectra of abiosample having a pH between about 5.15 to 5.53 are electronicallyanalyzed (block 160). A curve-fitting function (that can selectivelyapply one or more of a plurality of predefined basis functions to usezero, one, two or three neighboring peaks) can be applied to a definedpeak region in the NMR spectrum at about 3.2 to 3.4 (block 170). Anamount of TMAO in the biosample is calculated (block 175).

In some embodiments, a defined pH stable reference peak region in theNMR spectrum can be identified (block 161). A distance from thereference peak region to a calibration peak region can be calculated(block 163). The TMAO peak location can be determined using a defineddistance from the calibration peak (block 164).

The reference peak region can be a (anomeric) glucose peak region (block162).

In some embodiments, a defined calibration peak multiplet region can beused to determine the location of the TMAO peak. The calibration peakmultiplet has peaks that vary in distance apart based on pH of thebiosample variable (block 169).

The calibration peak region can be a citrate peak region (block 165).

The method may include adding a pH buffer to the biosample, thenacquiring FID signal of the sample in an NMR flow probe (166) with an ATof less than about 4 seconds per scan with a plurality of scans perbiosample. The TMAO peak region can have a peak with a width, onaverage, of about 1.2 Hz (block 172) (and can vary to be between about0.6 Hz and 2 Hz per biosample). A fitting function can be applied to theTMAO peak region with a line width of under 2 Hz, typically about 1 Hz(block 174).

The position of a TMAO peak region can be calculated using a fittingregion having a size between 50-100 data points based on a location of acitrate reference peak or peaks, then reducing the fitting region toabout 30-50 data points centered about the calculated location of theTMAO peak. TMAO peak region can be analyzed with a defined curve fittingfunction or functions that can selectively allow for one or moreneighbors on either side to account for small misalignments to determinethe level of TMAO.

As noted above, the probable actual TMAO peak location can be identifiedby weighting the region around the expected TMAO peak location with aGaussian, triangular parabolic or similar function. The probable actualTMAO peak location can then be identified as the highest weighted datapoint used to center the curve fitting region for determining the levelof TMAO.

FIGS. 14B, 14C and 14D illustrate an exemplary (and optional) automatedpre-analytical quality control evaluation 175 that can be carried outprogrammatically (e.g., by the analyzer 22 or a processor incommunication with the analyzer 22) before performing a clinical assay,e.g., the TMAO assay according to some embodiments. Thus, the TMAO assayis not performed if the input spectra fail pre-analytical qualitycontrol checks. The pre-analytical quality control evaluation 175evaluates input spectra to determine acquisition occurred or will occurunder specified conditions:

-   -   Sample is properly mixed and the injected sample completely        fills the flow cell    -   Magnetic field is homogeneous    -   Water suppression is adequate    -   Sample pH is within a defined range, e.g., 5.3±0.1.

Evaluation of these conditions is based on characterization of areference peak in the spectra. In the case of the TMAO assay, thecitrate peak serves as the pH sensitive reference peak. The height,integral, linewidth and skew of one of the citrate peaks is evaluated.This information is used as shown in the flowchart shown in FIG. 14B toidentify problems with mixing, injection and shimming.

As shown in FIG. 14B, a reference peak height is evaluated (shown ascitrate) block 176. For ease of discussion, the term “citrate” will beused for this evaluation, but other reference peaks may also be used. Ifit is high, this indicates “bad mixing” or too much serum. If low, thecitrate peak integral is obtained (block 177). If the integral isacceptable (within defined values), this indicates “bad” shimming andcan alert an operator via an output 175 o (audio and visual or justvisual) on the display 22D to initiate a shim operation or generate analert that identifies the problem. If low, a linewidth is taken at 50%,20% and 10% (block 178), if acceptable, this indicates “bad mixing”associated with the sample and again the analyzer 22 can alert anoperator of the problem or flag via an output on the display 22D and/orinitiate a retest. If high, skew can be evaluated (block 179). If it isright of a defined location, a “bad injection and bad shimming” alertcan be generated to the operator (e.g., via display 22D) and if it is tothe left, only a “bad injection alert is generated.

The programmatic evaluation 175 can determine the followingcharacteristics of the reference (e.g., citrate peak or peaks) for eachinput spectra. Height of one of the citric acid peaks, typically thesecond peak from the left (marked with asterisk * in FIG. 14C) can bedetermined. Linewidth of one peak, typically the second citric acid peakfrom the left at 50%/20%/10% peak height (FIG. 14D) and the integral ofall four citric acid peaks (FIG. 14C) can be determined. Skew can bedetermined at 10% of second citric acid peak from the left usingskew=y/2−x (FIG. 14D).

Bad shimming can identified if the citrate height is <97 au and has anacceptable citrate peak integral between 3674 and 3314 for any of theinput spectra. The low height indicates that the peak is too broadindicating poor field homogeneity.

Bad mixing can be identified if the citrate height is <97 au, citrateintegral <3314 au, acceptable line-width of <1.65/3.15/4.74 Hz (at50%/20%/10% peak height, respectively) for any of the input spectra.Other threshold values may be appropriate under different sample and/ordata acquisition conditions.

Bad injection or bad shimming can be identified if the citrate height is<97 au, citrate integral <3314 au, the line-widths are >1.65/3.15/4.74Hz (at 50%/20%/10% peak height, respectively), and 10% skew is to theright more than +0.09 to +0.32 Hz for any of the input spectra. Otherthreshold values may be appropriate under different sample and/or dataacquisition conditions

Bad injection can be identified if the citrate height is <97 au, citrateintegral <3314 au, the line-widths are >1.65/3.15/4.74 Hz (at50%/20%/10% peak height, respectively), and 10% skew is to the left morethan −0.09 to −0.32 Hz for any of the input spectra. Other thresholdvalues may be appropriate under different sample and/or data acquisitionconditions

It is noted that the flowcharts and block diagrams of certain of thefigures herein illustrate the architecture, functionality, and operationof possible implementations of analysis models and evaluation systemsand/or programs according to the present invention. In this regard, eachblock in the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

Referring now to FIG. 15A, it is contemplated that the TMAO analysis canbe carried out using a system 100 with at least one NMR clinicalanalyzer 22 as described, for example, with respect to FIG. 15B belowand/or in U.S. Pat. No. 8,013,602, the contents of which are herebyincorporated by reference as if recited in full herein.

The system 100 can include a NMR TMAO analysis module and/or circuit 200that can be onboard the analyzer 22 or at least partially remote fromthe analyzer 22. If the latter, the analysis module or circuit 200 canreside totally or partially on a server 150. The server 150 can beprovided using cloud computing which includes the provision ofcomputational resources on demand via a computer network. The resourcescan be embodied as various infrastructure services (e.g. computer,storage, etc.) as well as applications, databases, file services, email,etc. In the traditional model of computing, both data and software aretypically fully contained on the user's computer; in cloud computing,the user's computer may contain little software or data (perhaps anoperating system and/or web browser), and may serve as little more thana display terminal for processes occurring on a network of externalcomputers. A cloud computing service (or an aggregation of multiplecloud resources) may be generally referred to as the “Cloud”. Cloudstorage may include a model of networked computer data storage wheredata is stored on multiple virtual servers, rather than being hosted onone or more dedicated servers. Data transfer can be encrypted and can bedone via the Internet using any appropriate firewalls to comply withindustry or regulatory standards such as HIPAA. The term “HIPAA” refersto the United States laws defined by the Health Insurance Portabilityand Accountability Act. The patient data can include an accession numberor identifier, gender, age and test data.

As shown in FIG. 15B, the at least one analyzer 22 can be configured toevaluate containers 25 of biosamples 123 optionally including one ormore defined diluents or buffers 124. The containers 25 can havesample/buffer solutions and may be flowably introduced to the NMR probeusing a flow cell or the containers can be placed in the NMR probe forevaluation. The containers 25 can include respective biosamples of humanblood plasma or serum with a solution of citrate acid and sodium dibasicphosphate in a defined ratio, the ratio being between 25:75 to about50:50 (buffer:serum) by volume, with the pH being between about 5.15 and5.53.

The results of the analysis can be transmitted via a computer network,such as the Internet, via email or the like to a patient, clinician site50, to a health insurance agency or a pharmacy. The results can be sentdirectly from the analysis site (Site 1, Site 2) or may be sentindirectly via a central or distributed network (Site 3). The resultsmay be printed out and sent via conventional mail. This information canalso be transmitted to pharmacies and/or medical insurance companies,and/or respective patients. The results can be sent to a patient viaemail to a “home” computer or to a pervasive computing device such as asmart phone or notepad and the like. The results can be as an emailattachment of the overall report or as a text message alert, forexample.

The systems can be configured to measure different biosamples forassessing TMAO levels. For example, both urine and blood samples can beanalyzed and measurements reported together or separately with anyassociated risk or as an independent measurement.

Referring now to FIG. 16, a system 207 for acquiring and calculating thelineshape of a selected sample is illustrated. The system 207 includesan NMR spectrometer 22 for taking NMR measurements of a sample. In oneembodiment, the spectrometer 22 is configured so that the NMRmeasurements are conducted at about 400 MHz for proton signals; in otherembodiments the measurements may be carried out at between 200-900 MHzor other suitable frequency. Other frequencies corresponding to adesired operational magnetic field strength may also be employed.Typically, a proton flow probe is installed, as is a temperaturecontroller to maintain the sample temperature at about 47+/−0.5 degreesC. However, as noted above, other sample temperatures may be employed.The spectrometer 22 is controlled by a digital computer 214 or othersignal processing unit. The computer 211 should be capable of performingrapid Fourier transformations. It may also include a data link 212 toanother processor or computer 213, and a direct-memory-access channel214 which can connects to a hard memory storage unit 215.

The digital computer 211 may also include a set of analog-to-digitalconverters, digital-to-analog converters and slow device I/O ports whichconnect through a pulse control and interface circuit 216 to theoperating elements of the spectrometer. These elements include an RFtransmitter 217 which produces an RF excitation pulse of the duration,frequency and magnitude directed by the digital computer 211, and an RFpower amplifier 218 which amplifies the pulse and couples it to the RFtransmit coil 219 that surrounds sample cell 220. The NMR signalproduced by the excited sample in the presence of a 9.4 Tesla polarizingmagnetic field produced by superconducting magnet 221 is received by acoil 222 and applied to an RF receiver 223. The amplified and filteredNMR signal is demodulated at 224 and the resulting quadrature signalsare applied to the interface circuit 216 where they are digitized andinput through the digital computer 211. The TMAO analyzer circuit 200and/or module 350 (FIGS. 15A and 17) can be located in one or moreprocessors associated with the digital computer 211 and/or in asecondary computer 213 or other computers that may be on-site or remote,accessible via a worldwide network such as the Internet 227.

The TMAO analyzer circuit 200 can include a database of experimentaldeterminations of concentrations of TMAO to curves and/or areas of TMAOpeak regions for different levels of expected TMAO values in biologicranges as is known to those of skill in the art. Reference standards canbe used for calibration or defining concentrations of NMR measurements.The TMAO experimental or reference samples can be obtained from knownsuppliers of “high purity” TMAO material (e.g., Sigma-Aldrich, LLC.).The TMAO analyzer circuit 200 can include a TMAO basis function thataccounts for the residual protein baseline interferences that maysurvive the pulse sequence (e.g., the CPMG pulse sequence). The TMAObasis function can be an experimentally acquired spectrum of TMAOprocessed with defined (consistent) parameters to the actual spectrum.Computationally derived TMAO basis functions may also be used (e.g.,specified Lorentzians, Gaussians or mixed functions) or combinations ofsame.

After the NMR data are acquired from the biosample in the measurementcell 220, processing by the computer 211 produces another file that can,as desired, be stored in the storage database 215. This second file is adigital representation of the chemical shift spectrum and it issubsequently read out to the computer 213 for storage in its storage 225or a database associated with one or more servers. Under the directionof a program stored in its memory, or in another database or circuit incommunication with the NMR analyzer 22 (or spectrometer), one or moreprocessors, such as one associated with the computer 213, which may be apersonal, laptop, desktop, workstation, notepad, tablet or othercomputer, processes the chemical shift spectrum in accordance with theteachings of the present invention to generate a report which may beoutput to a printer 226 or electronically stored and relayed to adesired server, database(s), email address or URL. Those skilled in thisart will recognize that other output devices, such as a computer displayscreen, notepad, smart phone and the like, may also be employed for thedisplay of results.

It should be apparent to those skilled in the art that the functionsperformed by the computer 213 and its separate storage 225 may also beincorporated into the functions performed by the spectrometer's digitalcomputer 211. In such case, the printer 226 may be connected directly tothe digital computer 211. Other interfaces and output devices may alsobe employed, as are well-known to those skilled in this art.

Embodiments of the present invention may take the form of an entirelysoftware embodiment or an embodiment combining software and hardwareaspects, all generally referred to herein as a “circuit” or “module.”

As will be appreciated by one of skill in the art, the present inventionmay be embodied as an apparatus, a method, data or signal processingsystem, or computer program product. Accordingly, the present inventionmay take the form of an entirely software embodiment, or an embodimentcombining software and hardware aspects. Furthermore, certainembodiments of the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputer-usable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

The computer-usable or computer-readable medium may be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, device, or propagation medium. Morespecific examples (a non-exhaustive list) of the computer-readablemedium would include the following: an electrical connection having oneor more wires, a portable computer diskette, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, and a portable compactdisc read-only memory (CD-ROM). Note that the computer-usable orcomputer-readable medium could even be paper or another suitable medium,upon which the program is printed, as the program can be electronicallycaptured, via, for instance, optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java7, Smalltalk, Python, Labview, C++, or VisualBasic. However, thecomputer program code for carrying out operations of the presentinvention may also be written in conventional procedural programminglanguages, such as the “C” programming language or even assemblylanguage. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN), awide area network (WAN), a secure area network (SAN) or the connectionmay be made to an external computer (for example, through the Internetusing an Internet Service Provider).

FIG. 17 is a block diagram of exemplary embodiments of data processingsystems that illustrates systems, methods, and computer program productsin accordance with embodiments of the present invention. The processor310 communicates with the memory 314 via an address/data bus 348. Theprocessor 310 can be any commercially available or custommicroprocessor. The memory 314 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 305. Thememory 314 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 17, the memory 314 may include several categories ofsoftware and data used in the data processing system 305: the operatingsystem 352; the application programs 354; the input/output (I/O) devicedrivers 358; a TMAO Evaluation Module 350; and the data 356. The TMAOEvaluation Module 350 can interrogate one or more defined NMR signalpeak regions in proton NMR spectra of a respective biosample to identifya level of TMAO. As noted above, the TMAO Evaluation Module 350 mayidentify a reference peak region (pH stable) and a calibration peakregion (pH variable) to more precisely locate the TMAO peak region basedon a predetermined relationship of distance between the calibration,reference and TMAO peak regions. In some particular embodiments, theTMAO Module 350 can also subtract a known TMAO standard concentrationfrom a calculated concentration of the TMAO based on the amount of TMAOstandard added to the biosample to amplify the TMAO peak.

The data 356 may include signal (constituent and/or composite spectrumlineshape) data 362 which may be obtained from a data or signalacquisition system 320. As will be appreciated by those of skill in theart, the operating system 352 may be any operating system suitable foruse with a data processing system, such as OS/2, AIX or OS/390 fromInternational Business Machines Corporation, Armonk, N.Y., WindowsCE,WindowsNT, Windows95, Windows98, Windows2000 or WindowsXP from MicrosoftCorporation, Redmond, Wash., PalmOS from Palm, Inc., MacOS from AppleComputer, UNIX, FreeBSD, or Linux, proprietary operating systems ordedicated operating systems, for example, for embedded data processingsystems.

The I/O device drivers 358 typically include software routines accessedthrough the operating system 352 by the application programs 354 tocommunicate with devices such as I/O data port(s), data storage 356 andcertain memory 314 components and/or the image acquisition system 320.The application programs 354 are illustrative of the programs thatimplement the various features of the data processing system 305 and caninclude at least one application, which supports operations according toembodiments of the present invention. Finally, the data 356 representsthe static and dynamic data used by the application programs 354, theoperating system 352, the I/O device drivers 358, and other softwareprograms that may reside in the memory 314.

While the present invention is illustrated, for example, with referenceto the Module 350 being an application program in FIG. 17, as will beappreciated by those of skill in the art, other configurations may alsobe utilized while still benefiting from the teachings of the presentinvention. For example, the TMAO Module 350 may also be incorporatedinto the operating system 352, the I/O device drivers 358 or other suchlogical division of the data processing system 305. Thus, the presentinvention should not be construed as limited to the configuration ofFIG. 17, which is intended to encompass any configuration capable ofcarrying out the operations described herein.

In certain embodiments, the Module 350 includes computer program codefor providing a level of TMAO which may be used to assess CHD riskand/or to indicate whether therapy intervention is desired and/or trackefficacy of a therapy. The TMAO test may be used in conjunction withclinical evaluation and other diagnostic tests as an aid in assessing apatients risk for developing cardiovascular disease (CVD) or coronaryheart disease (CHD).

The NMR evaluated TMAO can be a useful companion diagnostic tnutritional guidance in the use of prebioticprobiotic containing foodsor supplements or other types of functional foods.

TMAO may also provide valuable information for other clinicalapplications including therapeutic monitoring and/or management. TMAOmeasurements can be used for management associated with specific diet,probiotic or drug treatment(s). TMAO measurements can be used withclinical trials and/or drug development programs. The TMAO measurementscan be used to contradict a planned or actual therapy.

TMAO may be used to monitor for signs or diagnosis of kidney transplantrejection. Metabolic profiling evaluations of kidney transplants haverevealed biomarkers that include altered levels oftrimethylamine-N-oxide (TMAO), dimethylamine, lactate, acetate andalanine. In many of these investigations, TMAO was increased by a factorof 3-4 compared to healthy controls. The increase in TMAO is believed tostabilize proteins when there is an increased concentration of proteindenaturants such as urea and guanidine derivatives following a toxicinsult to the kidney.

TMAO measurements may also be used to evaluate patients havingTrimethylaminuria (TMAU), also known as fish odor syndrome or fishmalodor syndrome. TMAU is a rare metabolic disorder that causes a defectin the normal production of the enzyme Flavin containing monooxygenase 3(FMO3). When FMO3 is not working correctly or if not enough enzyme isproduced, the body loses the ability to convert trimethylamine (TMA)from precursor compounds in food digestion into trimethylamine oxide(TMAO) through a process called N-oxygenation. Trimethylamine thenbuilds up and is released in the person's sweat, urine, and breath,giving off a strong fishy odor or strong body odor. Measurement of urinefor the ratio of trimethylamine to trimethylamine oxide is the standardscreening test.

FIG. 18 illustrates patient reports 400 that may include visual indiciaof risk 405 associated with TMAO measurements. FIG. 18 illustrates threepotential embodiments of the TMAO risk report dependent upon the lowerlimit of quantitation that is achievable due to analytical considerationand/or what is appropriate based on biological variability, particularlyassociated with blood plasma or serum samples.

The visual indicia 405 can include a graphic with a degree of riskindicated in a defined color scale. The color scale may range from“green”, “yellow” and “red or orange” for a continuum of risk from low(green) to higher risk (red/orange). The red or orange is indicated bythe cross-hatch markings while the yellow is shown by the lighter grayscale. Green or another low risk color can be used for the Q1, T1 or H1(lower half) values. Other colors may be used to visually denote risk.The report 400 may be generated with quantitative results only for theupper 3 quartiles, the upper two tertiles or the upper 2 quartiles. Asshown in the three exemplary visual risk indicia formats, a demarcationline 410 can separate the lower range values from the upper ranges. Inother embodiments, a quantitative result can be provided for allquartiles or tertiles of measurements.

FIG. 19 is an NMR (chemical shift/ppm) spectrum of urine according toembodiments of the present invention. The urinary TMAO concentration istypically much higher than in serum, but the range is quite large. Urineis also in a “crowded” matrix and there is a large number of potentiallyconfounding metabolites. As in the serum matrix, adjustment of the pHcan be used to move the TMAO peak into more or less crowded regions.FIG. 19 illustrates an expansion of the TMAO region of a ¹H NMR spectrumof urine. The samples were prepared with a 60:40 mixture of urine andNMR diluent with the pH starting at 4.62 at the bottom and going to 6.83in the top; pH step size is approximately 0.15 units.

The location of the TMAO peak can be reliably determined by using either(or both) the creatinine peak labeled in the FIG. 19 or the citratepeak. Both creatinine and citrate are endogenous metabolites in urineand so this pH referencing could be carried out without the addition ofcorresponding compounds in the buffer or diluent. The relationshipbetween the TMAO peak location and the respective citrate and creatininepeak locations are show in FIGS. 20A and 20B. Both graphs show a nearlylinear correlation between the TMAO and the respective potentialreference peak. It is noted that the exact nature of the relationshipbetween the TMAO peak location and the creatinine and citrate can bedependent upon the sample composition, i.e., ratio of urine to buffer.

Quantitation of the TMAO concentration of the urine and other biosamples(e.g., blood plasma and serum) can be achieved by modeling the TMAOsignal with computationally derived functions. The baseline can bemodeled by DC offset, linear, and/or quadratic functions. The TMAO peakcan be modeled by Lorentzian and/or Gaussian functions of varying linewidths. These “mixed” basis set functions can have the identical height(FIG. 21) or be normalized by area (FIG. 22).

FIG. 21 shows a basis set of Lorentzian and Gaussian functions to modelTMAO signal. The height of each function is identical. DC offset andquadratic functions can be used to model baseline signal.

FIG. 22 shows a normalized basis set of Lorentzian and Gaussianfunctions to model TMAO signal. The area of each function is identical.DC offset and quadratic functions used to model baseline signal are alsonormalized to the same area.

FIGS. 23-26 are graphs of additional examples of basis sets. FIG. 23shows Lorentzian functions with identical height and varying line width.FIG. 24 shows Gaussian functions with identical height and varying linewidth. FIG. 25 shows Lorentzian functions with normalized area andvarying line width. FIG. 26 shows Gaussian functions with normalizedarea and varying line width.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. In the claims, means-plus-function clauses, where used, areintended to cover the structures described herein as performing therecited function and not only structural equivalents but also equivalentstructures. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

1. (canceled)
 2. The method of claim 7, further comprising:electronically identifying a pH-stable reference peak region in the NMRspectrum of the biosample; electronically identifying a definedcalibration peak region in the NMR spectrum of the biosample, whereinthe calibration peak region has a location that changes based on pH ofthe biosample; and electronically calculating a distance between thereference and calibration peak regions; then electronically determininga location of the TMAO peak for the defined TMAO peak region based onthe calculated distance.
 3. The method of claim 2, wherein theelectronically determining the TMAO peak region location is carried outusing a defined relationship of location of the reference peak region tolocation of the calibration peak region.
 4. The method of claim 7,wherein the biosample is a blood plasma or serum wherein the definedTMAO peak is at about 3.30 ppm. 5-6. (canceled)
 7. A method ofdetermining a measure of TMAO in in vitro biosamples, comprising:electronically determining a level of trimethylamine-N-oxide (“TMAO”) ofan in vitro biosample using a defined TMAO peak region having a singleTMAO peak residing between about 3.2 and 3.4 ppm of a proton NMRspectrum, wherein the electronic determination of the level of TMAO iscarried out using a defined relationship between a location of areference peak or peaks and a location of a pH sensitive calibrationpeak or peaks, and an expected TMAO peak location, wherein a probableactual TMAO peak location is then identified by: electronicallyweighting a region around the expected TMAO peak location with a definedfunction; then electronically identifying a highest weighted data pointof the weighted region; then electronically identifying a probableactual TMAO peak location corresponding to location of the highestweighted data point.
 8. The method of claim 7, wherein the definedrelationship is a defined linear relationship.
 9. The method of claim 7,wherein after the identification of the probable actual TMAO peaklocation, the method further comprises applying a curve fitting functionor functions to a curve fitting region of about 30 to about 50 datapoints centered about the identified probable actual TMAO peak locationto determine the level of TMAO.
 10. The method of claim 8, wherein thecurve fitting function or functions can selectively allow for one ormore neighbors on either side of the probable actual TMAO peak locationto account for small misalignments to determine the level of TMAO. 11.The method of claim 7, wherein the electronically determining the levelof TMAO is carried out to generate a measurement that is substantiallylinear in a biological range of between at least about 1-50 μM. 12-23.(canceled)
 24. The computer program product of claim 32, furthercomprising: computer readable program code that identifies a pH-stablereference peak region in the NMR spectrum of the biosample; computerreadable program code that identifies a defined calibration peak regionin the NMR spectrum of the biosample; computer readable program codethat calculates a distance between the reference and calibration peakregions; and computer readable program code that determines a positionof the TMAO peak region based on the calculated reference andcalibration peak region distance.
 25. The computer program product ofclaim 24, wherein the computer readable program code that determines theposition of the TMAO peak region includes computer readable program codethat employs a defined mathematical relationship of a location of thereference peak to a location of the calibration peak and the location ofthe calibration peak to a location of the TMAO peak, and wherein thecalibration and TMAO peak region locations vary according to pH of thebiosample. 26-31. (canceled)
 32. A computer program product forevaluating in vitro biosamples, the computer program product comprising:a non-transitory computer readable storage medium having computerreadable program code embodied in the medium, the computer-readableprogram code comprising: computer readable program code that evaluatesNMR signal in at least one defined peak region that includes atrimethylamine-N-oxide (TMAO) peak region residing between about 3.2 and3.4 ppm of a proton NMR spectrum of an in vitro biosample to determine alevel of TMAO, wherein the computer readable program code that evaluatesthe TMAO peak region to determine the level of TMAO includes: computerreadable program code that weights a region around an expected TMAO peaklocation with a defined function; computer readable program code thatidentifies a highest weighted data point of the weighted region as aprobable actual TMAO peak location; and computer readable program codethat applies a curve fitting function or functions to a curve fittingregion of about 30 to about 50 data points centered about the identifiedprobable actual TMAO peak location to determine a level of TMAO.
 33. Themethod of claim 32, wherein the computer program code that applies thecurve fitting function or functions can selectively allow for one ormore neighbors on either side of the probable actual TMAO peak locationto account for small misalignments to determine the level of TMAO. 34.(canceled)
 35. The system of claim 50, wherein the at least oneprocessor is configured to (i) identify a pH-stable reference peakregion in the at least one NMR spectrum of the biosample; (ii) identifya defined calibration peak region in the at least one NMR spectrum ofthe biosample; (iii) calculate a distance between the reference andcalibration peak regions; then (iv) determine a location of a singleTMAO peak of the defined TMAO peak region based on the calculateddistance.
 36. The system of claim 50, wherein a location of the definedTMAO peak region is determined using a defined mathematical relationshipthat correlates a location of the calibration peak with a location ofthe TMAO peak, both of which vary according to pH of the biosample,relative to a calculated distance between the calibration peak or peaksand at least one reference peak. 37-40. (canceled)
 41. The system ofclaim 50, wherein the at least one processor is configured to generatemeasurements that are substantially linear in a biological range ofbetween about 1-50 μM.
 42. The system of claim 50, wherein the referencepeak region is associated with anomeric glucose at about 5.20 ppm, andwherein the calibration peak region is associated with one or more peaksof a citrate multiplet. 43-49. (canceled)
 50. An analysis system,comprising: at least one NMR spectrometer for acquiring at least oneproton NMR spectrum of an in vitro biosample; and at least one processorin communication with the at least one NMR spectrometer, the at leastone processor configured to determine a level of trimethylamine-N-oxide(“TMAO”) in the biosample using the at least one proton NMR spectrumbased on a defined TMAO peak region residing between about 3.2 and 3.4ppm of the at least one proton NMR spectrum, wherein the at least oneprocessor is configured to identify an expected TMAO peak location usinga defined relationship between a location of a reference peak or peaksand a location of a pH sensitive calibration peak or peaks, and whereinthe at least one processor is configured to identify a probable actualTMAO peak location by (i) weighting a region around the expected TMAOpeak location with a defined function; then (ii) identify a highestweighted data point of the weighted region as the probable actual TMAOpeak location.
 51. The system of claim 50, wherein the at least oneprocessor is configured to apply a curve fitting function or functionsto a curve fitting region of about 30 to about 50 data points centeredabout the identified probable actual TMAO peak location to determine thelevel of TMAO.
 52. The system of claim 51, wherein the at least oneprocessor is configured to apply the curve fitting function or functionsto selectively allow for one or more neighbors on either side of theprobable actual TMAO peak location to account for small misalignments todetermine the level of TMAO.
 53. The system of claim 50, wherein thedefined relationship is a defined linear relationship.